f = lambda k, l: (k + len(l)) * 47 % 11 + 1 #lambda function to calculate task parameters and to choose our representative print(f(30, 'Perina')) print(f(30, 'Nezdara')) print(f(3, 'Kondac'))

import numpy as np import pandas as pd from plotly import graph_objects as go import plotly.express as px from plotly.subplots import make_subplots np.set_printoptions(precision=5, suppress=True)

df = pd.read_csv('./df.csv', index_col=False, sep=';', decimal=',', dtype={'Weight': float, 'Status': str}) #load the original dataset df.head()

""" split the original dataset into two subsets based on sparrows status, then drop the 'Status' column """ survived = df[df['Status'] == 'survived'].copy() survived.drop(columns=['Status'], inplace=True) perished = df[df['Status'] == 'perished'].copy() perished.drop(columns=['Status'], inplace=True)

df.describe(include='all')

survived.describe(include='all')

perished.describe(include='all')

def mean_estimate(values) -> float: """ function to calculate the mean, given a list of values """ return sum(values) / len(values)

survived_mean = mean_estimate(survived['Weight']) perished_mean = mean_estimate(perished['Weight']) print(f"The mean value estimate for 'Survived' class is {survived_mean:.3f} and {perished_mean:.3f} for the 'Perished' class.")

def variance_estimate(values) -> float: """ function to calculate the variance, given a list of values """ return sum((values-mean_estimate(values))**2 / (len(values)-1))

variance_survived = variance_estimate(survived['Weight']) variance_perished = variance_estimate(perished['Weight']) print(f"The variance value estimate for 'Survived' is {variance_survived:.3f} and {variance_perished:.3f} for 'Perished' class.")

def median(values): """ function to calculate the median value, given a list of values """ length = len(values) values.sort() return values[length // 2] if length % 2 != 0 else sum(values[length//2-1:length//2+1])/2

median_survived = median(survived['Weight'].tolist()) median_perished = median(perished['Weight'].tolist()) print(f'Median for survived is {median_survived:.3f} and median for perished is {median_perished:.3f}')

fig = px.histogram(df, x="Weight", color="Status", title="Histogram of sparrow weight", histnorm='probability density') fig.update_layout( xaxis_title_text='Weight (grams)', # xaxis label yaxis_title_text='Count', # yaxis label ) fig.show()

def edf(x, values: pd.Series) -> float: #edf - Empirical Distribution Function """ function that returns how many values in data are lower than x """ counts = values.value_counts() return sum(counts[counts.index<=x])/len(values)

x_s = np.linspace(survived.Weight.min(), survived.Weight.max(), 1000) y_s = [edf(x, survived.Weight) for x in x_s] x_p = np.linspace(perished.Weight.min(), perished.Weight.max(), 1000) y_p = [edf(x, perished.Weight) for x in x_p]

fig = make_subplots(rows=2, cols=2, subplot_titles=[f'{y} - {x}' for y in ['Histogram', 'Empirical Distribution Function'] for x in ['survived','perished']]); fig.append_trace(go.Histogram(x=survived['Weight'], name='Survived', histnorm='probability density'), 1, 1 ); fig.append_trace(go.Histogram(x=perished['Weight'], name='Perished', histnorm='probability density'), 1, 2 ); fig.update_xaxes(title_text="Weight (grams)", row=1, col=1); fig.update_yaxes(title_text="Count", row=1, col=1); fig.update_xaxes(title_text="Weight (grams)", row=1, col=2); fig.update_yaxes(title_text="Count", row=1, col=2); fig.add_trace(go.Scatter(x=x_s, y=y_s, name='Survived', mode='lines'), row=2, col=1); fig.add_trace(go.Scatter(x=x_p, y=y_p, name='Perished', mode='lines'), row=2, col=2); fig.update_xaxes(title_text="Weight", row=2, col=1); fig.update_yaxes(title_text="P(X <= x)", row=2, col=1); fig.update_xaxes(title_text="Weight", row=2, col=2); fig.update_yaxes(title_text="P(X <= x)", row=2, col=2); fig.update_layout(showlegend=False);

fig.show()

from scipy.stats import norm, uniform, expon fig = make_subplots(2, 1, shared_xaxes=True) for (i, y) in enumerate([('Survived', survived['Weight']), ('Perished', perished['Weight'])]): cat, values = y x = np.linspace(values.min()-3, values.max()+3, 1000) loc_exp, scale_exp = expon.fit(values) loc_norm, scale_norm = norm.fit(values) loc_unif, scale_unif = uniform.fit(values) # Rice Rule nbins = int(np.round(2.*np.cbrt(df.shape[0]))) fig.append_trace(go.Histogram(x=values, nbinsx=nbins, name=cat, histnorm='probability density'), i+1, 1 ) fig.append_trace(go.Scatter(x=x, y=expon.pdf(x, loc_exp, scale_exp), mode='lines', name=f'Exp({1/scale_exp:2.1f})'), i+1, 1 ) fig.append_trace(go.Scatter(x=x, y=uniform.pdf(x, loc_unif, scale_unif), mode='lines', name=f'Unif({loc_unif:2.2f},{scale_unif:2.2f})'), i+1, 1 ) fig.append_trace(go.Scatter(x=x, y=norm.pdf(x, loc_norm, scale_norm), mode='lines', name=f'N({loc_norm:2.2f}, {scale_norm**2:2.2f})'), i+1, 1 ) fig.update_layout(title='Histogram of each group with normal, exponential and uniform distribution functions') fig.update_xaxes(title_text="Weight") fig.update_yaxes(title_text="Count") fig.show()

min_of_two = lambda x,y : min(x.min(), y.min()) max_of_two = lambda x,y : max(x.max(), y.max())

def rand_val_dist(name, data, n=100): """ function generates 100 random values by ours distribution and then shows their histogram """ loc_s, scale_s = norm.fit(data['Weight']) rs = np.random.normal(loc=loc_s, scale=scale_s, size=n) fig = go.Figure() x = np.linspace(min_of_two(rs, data.Weight)-1, max_of_two(rs, data.Weight)+1, 1000) fig.add_trace(go.Histogram(x=data.Weight, nbinsx=11, name=name, histnorm='probability density')) fig.add_trace(go.Histogram(x=rs, nbinsx=11, name='Generated', histnorm='probability density')) fig.add_trace(go.Scatter(x=x, y=norm.pdf(x, loc_s, scale_s), mode='lines', name=f'N({loc_s:2.2f}, {scale_s**2:2.2f})') ) fig.update_layout(title="Generated samples from "+name+" distribution") fig.update_xaxes(title_text="Weight") fig.update_yaxes(title_text="Count") fig.show()

rand_val_dist('Survived', survived)

rand_val_dist('Perished', perished)

confidence_level = 95 alpha = lambda x: np.round(1 - (x/100), 5) a = alpha(confidence_level) / 2 print(f'alpha: {a}')

Z_ah = norm.isf(a) sigma = np.sqrt(variance_estimate(perished.Weight)) mu = mean_estimate(perished.Weight) n_sqrt = np.sqrt(perished.Weight.shape[0]) loc_p, scale_p = norm.fit(perished.Weight) """ use of the pattern """ p_lb, p_ub = mu - Z_ah*(sigma/n_sqrt), mu + Z_ah*(sigma/n_sqrt) print(f'({p_lb}, {p_ub})')

fig = go.Figure() x = np.linspace(perished.Weight.min()-1, perished.Weight.max()+1, 1000) X = np.linspace(p_lb, p_ub, 1000) fig.add_trace(go.Scatter(x=X, y=norm.pdf(X, loc_p, scale_p),fill='tozeroy', mode='lines', name=f'Confidence inteval ([{p_lb:2.3f},{p_ub:2.3f}])', marker=dict( size=16, color='red', #set color equal to a variable showscale=True ))) fig.add_trace(go.Scatter(x=x, y=norm.pdf(x, loc_p, scale_p), mode='lines', name=f'N({loc_p:2.2f}, {scale_p**2:2.2f})', marker=dict( size=16, color='blue', #set color equal to a variable showscale=True )) ) fig.add_trace(go.Scatter(x=[mu, mu], y=[0,norm.pdf(mu, loc_p, scale_p)], mode='lines', name=f'mu ({mu:2.3f})', marker=dict( size=16, color='lime', #set color equal to a variable showscale=True )) ) fig.update_layout(title="Two-tail 95% confidence interval for Perished") fig.update_xaxes(title_text="Weight") fig.update_yaxes(title_text="Count") fig.show()

Z_ah = norm.isf(a) sigma = np.sqrt(variance_estimate(survived.Weight)) mu = mean_estimate(survived.Weight) n_sqrt = np.sqrt(survived.Weight.shape[0]) loc_s, scale_s = norm.fit(survived.Weight) s_lb, s_ub = mu - Z_ah*(sigma/n_sqrt), mu + Z_ah*(sigma/n_sqrt) print(f'({s_lb}, {s_ub})')

fig = go.Figure() x = np.linspace(survived.Weight.min()-1, survived.Weight.max()+1, 1000) X = np.linspace(s_lb, s_ub, 1000) fig.add_trace(go.Scatter(x=X, y=norm.pdf(X, loc_s, scale_s),fill='tozeroy', mode='lines', name=f'Confidence inteval ([{p_lb:2.3f},{p_ub:2.3f}])', marker=dict( size=16, color='red', #set color equal to a variable showscale=True ))) fig.add_trace(go.Scatter(x=x, y=norm.pdf(x, loc_s, scale_s), mode='lines', name=f'N({loc_p:2.2f}, {scale_p**2:2.2f})', marker=dict( size=16, color='blue', #set color equal to a variable showscale=True )) ) fig.add_trace(go.Scatter(x=[mu, mu], y=[0,norm.pdf(mu, loc_s, scale_s)], mode='lines', name=f'mu ({mu:2.3f})', marker=dict( size=16, color='lime', #set color equal to a variable showscale=True )) ) fig.update_layout(title="Two-tail 95% confidence interval for Survived") fig.update_xaxes(title_text="Weight") fig.update_yaxes(title_text="Count") fig.show()

K = 30 in_range = lambda x,l,u: ((x >= l) and (x <= u)) accepted = in_range(K, p_lb, p_ub) print(f'Category: Perished\nK:= {K} is {"not" if not accepted else ""} in the \ range of confidence interval ({p_lb:2.3f}, {p_ub:2.3f}). Thus, we {"reject" if not accepted else "accept"} the hypothesis H_0.\n') accepted = in_range(K, s_lb, s_ub) print(f'Category: Survived\nK:= {K} is {"not" if not accepted else ""} in the \ range of confidence interval ({s_lb:2.3f}, {s_ub:2.3f}). Thus, we {"reject" if not accepted else "accept"} the hypothesis H_0.')

alpha = 0.05 print(f'Variance of the Perished subset: {variance_perished:2.3f} and variance of the Survived subset: {variance_survived:2.3f}') equal = variance_perished == variance_survived tests = ["Welch's","Standard independent two sample"] print(f'Variances are {"not" if not equal else ""} equal, thus we are going to use {tests[equal]} test for testing whether or not the two groups share the same mean value.')

from scipy.stats import ttest_ind stat, p_value = ttest_ind(survived.Weight, perished.Weight, equal_var=False) print(f'Based on the Welch\'s t-test, we {"reject" if p_value<alpha else "accept"} the hypothesis H_0.')